Measuring traffic flow and classifying vehicle types: A surveillance video based approach

Authors : Erhan İNCE

Pages : 607-620

View : 18 | Download : 7

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :The paper presents a vehicle counting method based on invariant moments and shadow aware foreground masks. Estimation of the background and the segmentation of foreground regions can be done using either the Mixture of Gaussians model insert ignore into journalissuearticles values(MoG); or an improved version of the Group Based Histogram insert ignore into journalissuearticles values(GBH); technique. The work demonstrates that, even though the improved GBH method delivers performance just as good as MoG, considering computational efficiency, MoG is more suitable. Shadow aware binary masks for each frame are formed by using background subtraction and shadow removal in the Hue Saturation and Value insert ignore into journalissuearticles values(HSV); domain. To determine new vehicles in the subsequent frame insert ignore into journalissuearticles values(in addition to those in the current frame);, invariant moments are used. For vehicles which are the same model and brand, color information and distance between center of mass and an imaginary reference line need to be considered. As for classification, the paper proposes a new method based on perspective projection of the scene geometry. The classification is grouped into three major tracks: bikes, saloon cars, and long vehicles. For each category, a lower and an upper bounding curve are developed to show the extent of their associated modality regions.
Keywords : Group based histogram, mixture of Gaussians, cast shadow removal, convex hull fitting, classification, modality regions